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通过结合人工神经网络和多重线性回归来预测电阻与导电模式
1Department of Smart Wearable Engineering, Soongsil University, Seoul 06978, Republic of Korea.
Polymers
|October 28, 2023
概括
线图案显著影响导电性线的阻力. 使用人工神经网络等模型,优化针长度和角度,可以提高织品和材料工程中的组件性能.
科学领域:
- 材料科学 材料科学 材料科学
- 织工程 织工程 织工程
- 电气工程 电气工程
背景情况:
- 导电线对于智能织品和电子元件至关重要.
- 了解制造过程如何影响电气性能对于可靠的设备性能至关重要.
研究的目的:
- 为了研究线图案对银糊涂导电线电阻的影响.
- 为了比较多重线性回归和人工神经网络模型对阻力变化的预测精度.
主要方法:
- 导电性结构和变异 (长度,角度) 的实验分析.
- 应用多重线性回归和人工神经网络 (ANN) 模型用于阻力预测.
- 拼接参数和线阻力之间的相关性分析.
主要成果:
- 线长度减少导致导电性线宽度增加.
- 角的变化直接影响了导电线的电阻值.
- 针的长度和角度都被确定为影响阻力的重要因素.
结论:
- 图案优化是控制导电线程电阻的关键.
- 安纳模型为预测和管理导电线程电阻提供了有价值的工具.
- 这些发现支持智能织品和电子元件设计方面的创新.
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